Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for activity monitoring using video data, the system comprising: at least one special purpose processor executing a plurality of video analysis worker processes; a management subsystem coupled to the at least one special purpose processor that performs specific tasks, wherein the management subsystem comprises: a feed management subsystem for managing video data from the plurality of video data sources; a worker management subsystem for managing the video analysis worker processes; a location management subsystem for managing geographic information related to the plurality of video sources; a data storage subsystem for managing storage of data, including unprocessed and processed video data; and a plurality of video data sources of multiple types producing video data of different types, wherein the plurality of video data sources comprises at least one mobile device executing a video sensing application that produces a video stream for processing by the plurality of video analysis worker processes; wherein the specific tasks include line analysis comprising using video data of people standing in a line to determine how many people wait in line and how long it takes a person to go through the line.
The system is designed for monitoring activities using video data from diverse sources, addressing the need for scalable, real-time analysis of video streams to extract actionable insights. The system includes at least one special-purpose processor executing multiple video analysis worker processes, each dedicated to processing video data. A management subsystem oversees these processes and includes several subcomponents: a feed management subsystem handles video data ingestion from various sources, a worker management subsystem coordinates the analysis tasks, a location management subsystem tracks geographic data related to video sources, and a data storage subsystem manages both raw and processed video data. The video sources include mobile devices running video sensing applications, producing streams for analysis. The system performs specific tasks, such as line analysis, where video data of people standing in a line is used to determine the number of individuals waiting and the time taken for each person to proceed through the line. This enables applications like queue monitoring in retail or public spaces, optimizing resource allocation and customer experience. The distributed architecture ensures efficient processing and scalability across multiple video sources and analysis tasks.
2. A system for activity monitoring using video data, the system comprising: at least one special purpose processor executing a plurality of video analysis worker processes; a management subsystem coupled to the at least one special purpose processor that performs specific tasks; and a plurality of video data sources of multiple types producing video data of different types, wherein the plurality of video data sources comprises at least one mobile device executing a video sensing application that produces a video stream for processing by the plurality of video analysis worker processes, and a user interface coupled to receive video data from the data storage subsystem and to present the video data in a human-accessible manner; wherein the specific tasks include line analysis comprising using video data of people standing in a line to determine how many people wait in line and how long it takes a person to go through the line.
This invention relates to a system for monitoring activities using video data from diverse sources. The system addresses the challenge of analyzing video streams from multiple types of devices to extract meaningful insights, such as tracking people in lines to measure wait times and queue lengths. The system includes at least one special-purpose processor that executes multiple video analysis worker processes to handle video data processing tasks. A management subsystem coordinates these processors, performing specific functions such as line analysis. The system integrates video data from various sources, including mobile devices running video sensing applications that generate video streams for analysis. Additionally, a user interface displays processed video data in a human-readable format. The line analysis function processes video data of people standing in lines to determine the number of individuals waiting and the time it takes for a person to move through the line. This enables real-time monitoring of queues, which can be useful in retail, transportation, or event management settings. The system's ability to handle multiple video types and sources ensures flexibility in deployment across different environments. The management subsystem ensures efficient coordination of processing tasks, while the user interface provides accessible visualization of the analyzed data.
3. A system for activity monitoring using video data, the system comprising: at least one special purpose processor executing a plurality of video analysis worker processes; a management subsystem coupled to the at least one special purpose processor that performs specific tasks; and a plurality of video data sources of multiple types producing video data of different types, wherein the plurality of video data sources comprises at least one mobile device executing a video sensing application that produces a video stream for processing by the plurality of video analysis worker processes, a contributor management subsystem for managing entities that control the plurality of video data sources, wherein entities comprise individuals, companies and organizations; wherein the specific tasks include line analysis comprising using video data of people standing in a line to determine how many people wait in line and how long it takes a person to go through the line.
This system monitors activities using video data from diverse sources, addressing challenges in tracking and analyzing real-time events across multiple environments. The system includes specialized processors running video analysis worker processes to handle video data from various sources, such as mobile devices running video sensing applications. A management subsystem coordinates these processors, performing tasks like line analysis, where video data of people in lines is processed to determine wait times and queue lengths. The system also includes a contributor management subsystem to oversee entities—individuals, companies, or organizations—that control the video data sources. The architecture supports scalable, real-time monitoring by distributing video analysis tasks across multiple worker processes, enabling efficient processing of heterogeneous video streams. The system is designed to extract actionable insights from video data, particularly in scenarios like crowd management, where understanding line dynamics is critical. By integrating mobile devices as video sources, the system leverages widely available technology to enhance data collection flexibility. The management subsystem ensures coordinated processing, while the contributor management subsystem facilitates controlled access and contribution from multiple entities.
4. A system for activity monitoring using video data, the system comprising: at least one special purpose processor executing a plurality of video analysis worker processes, wherein the plurality of video analysis worker processes comprise a process for detection of activity areas; wherein the process for detection of activity areas comprises: detecting movement in a current video frame; detecting foreground in a current video frame; and generating and updating a background model to generate an expected background; and a management subsystem coupled to the at least one special purpose processor that performs specific tasks; and a plurality of video data sources of multiple types producing video data of different types, wherein the plurality of video data sources comprises at least one mobile device executing a video sensing application that produces a video stream for processing by the plurality of video analysis worker processes; wherein the specific tasks include line analysis comprising using video data of a people standing in a line to determine how many people wait in line and how long it takes a person to go through the line.
This invention relates to a system for monitoring activities using video data, addressing the need for automated analysis of video streams from diverse sources to detect and quantify human activities, such as line management in queues. The system includes at least one special-purpose processor executing multiple video analysis worker processes, including a process for detecting activity areas. This process involves detecting movement and foreground objects in current video frames while dynamically generating and updating a background model to distinguish expected background elements from active regions. The system integrates video data from multiple sources, including mobile devices running video sensing applications, to produce heterogeneous video streams for analysis. A management subsystem coordinates these processes and performs specific tasks, such as line analysis, where video data of people standing in a line is used to determine the number of individuals waiting and the time taken for each person to progress through the line. The system leverages specialized hardware and software to process real-time video inputs, enabling efficient activity monitoring in dynamic environments. The invention aims to provide scalable, automated solutions for tracking and analyzing human behavior in various settings, such as retail, transportation, or public spaces, by combining motion detection, foreground extraction, and background modeling techniques.
5. The system of claim 4 wherein: the process for detection of activity areas comprises: detecting movement in a current video frame; detecting foreground in a current video frame; and generating and updating a background model to generate an expected background; and wherein the plurality of video analysis worker processes comprise a process for detection of activity areas results in activity contours to model a waiting line from the video data.
This invention relates to video analysis systems for detecting and modeling activity areas, such as waiting lines, in real-time video data. The system addresses the challenge of accurately identifying and tracking dynamic regions of interest, such as queues or crowds, in surveillance or monitoring environments. The system processes video frames to detect movement and foreground objects, then generates and updates a background model to distinguish between expected background and active foreground regions. By analyzing these detected activity areas, the system produces activity contours that define the boundaries of dynamic regions, such as waiting lines, enabling further analysis or decision-making. The system employs multiple video analysis worker processes to handle different aspects of the detection and modeling tasks, ensuring efficient and accurate processing of video data. The background model is continuously updated to adapt to environmental changes, improving the reliability of activity detection over time. This approach enhances situational awareness in applications like retail monitoring, crowd management, or security surveillance.
6. The system of claim 4 wherein: the process for detection of activity areas comprises: detecting movement in a current video frame; detecting foreground in a current video frame; and generating and updating a background model to generate an expected background; and wherein the plurality of video analysis worker processes comprise a process for detection of activity areas results in activity contours to model a waiting line from the video data, and further results in a curve model of the waiting line using line contours.
This invention relates to video analysis systems for detecting and modeling activity areas, particularly for monitoring waiting lines in video data. The system addresses the challenge of accurately identifying and tracking dynamic activity regions, such as queues, in real-time video streams. The process involves detecting movement and foreground objects in each video frame while continuously updating a background model to distinguish between expected background elements and active foreground regions. By analyzing these detected activity areas, the system generates activity contours that outline the boundaries of the waiting line. These contours are then used to construct a curve model representing the shape and structure of the waiting line, enabling precise monitoring and analysis of queue dynamics. The system employs multiple video analysis worker processes to handle different aspects of the detection and modeling tasks, ensuring efficient and accurate processing of the video data. This approach enhances the ability to track and manage waiting lines in various environments, such as retail stores, transportation hubs, or public venues, by providing real-time insights into queue behavior and flow.
7. The system of claim 4 wherein: the process for detection of activity areas comprises: detecting movement in a current video frame; detecting foreground in a current video frame; and generating and updating a background model to generate an expected background; and wherein the plurality of video analysis worker processes comprise a process for detection of activity areas results in activity contours to model a waiting line from the video data, and further results in a curve model of the waiting line using line contours, and wherein the plurality of video analysis workers generate a measurement of the waiting line in real time using the curve model and the line contours.
A video analysis system monitors and models waiting lines in real-time from video data. The system addresses the challenge of accurately detecting and measuring dynamic waiting lines in crowded environments, such as retail stores or public spaces, where manual monitoring is inefficient. The system processes video frames to detect movement and foreground objects, then generates and updates a background model to distinguish between expected background and active foreground regions. This detection process produces activity contours that outline the waiting line structure. The system further refines these contours into a curve model representing the waiting line's shape and position. Multiple video analysis worker processes collaborate to analyze the video data, ensuring real-time performance. The system continuously measures the waiting line's length and characteristics using the curve model and line contours, providing actionable insights for crowd management or service optimization. The approach improves upon traditional methods by automating line detection and measurement, reducing human error, and enabling real-time decision-making.
8. The system of claim 4 wherein: the process for detection of activity areas comprises: detecting movement in a current video frame; detecting foreground in a current video frame; and generating and updating a background model to generate an expected background; and wherein the plurality of video analysis worker processes comprise a process for detection of activity areas results in activity contours to model a waiting line from the video data, and further results in a curve model of the waiting line using line contours, and wherein the plurality of video analysis workers generate a measurement of the waiting line in real time using the curve model and the line contours, and wherein the measurement comprises a number of people in the waiting line, and an average wait time.
This invention relates to video-based systems for analyzing and measuring waiting lines in real time. The system addresses the challenge of accurately detecting and quantifying activity areas, such as waiting lines, in video data to provide actionable insights like line length and wait times. The system includes multiple video analysis worker processes that detect movement and foreground objects in video frames while continuously updating a background model to distinguish dynamic activity from static elements. These processes generate activity contours to model the waiting line, which are then used to create a curve model representing the line's shape. The system measures the waiting line in real time by analyzing the curve model and line contours, producing metrics such as the number of people in the line and the average wait time. This approach enables automated monitoring of queues in environments like retail stores, banks, or transportation hubs, improving operational efficiency and customer experience. The system dynamically adapts to changing conditions by refining the background model and activity detection, ensuring accurate measurements even in varying lighting or crowd densities.
Unknown
October 1, 2019
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